We now place a stochastic model on \(\nu \)  inspired by the approach taken in
[1]}, see also
[2]}, [3]}, [4]} for
similar Bayesian approaches in computational anatomy. The goal is to develop an
algorithm to infer \(\nu \) , passing via the deterministic problem seen above.
First, we present some preliminaries on the Bayesian approach to inverse
problems in section REF , essentially quoting concepts and results from
[5]}, or [6]} for an exposition of
algorithmic aspects of function space MCMC. Section REF 
then formally describes how we apply this stochastic approach to inverse
problems to \(\nu \)  by a finite-dimensional family of parameterisations.
